{spoiler=GameLab KBTU}
Video Game Development and Research Laboratory GameLab KBTU was established in June 2022 on the basis of the KBTU School of IT and Engineering. The main activities of the laboratory:
- Elective disciplines in the development and research of games within the framework of the bachelor's degree (from autumn 2022)
- Elective disciplines in the development and research of games within the framework of the master's program (from autumn 2023)
- Mentoring GameDev projects
- Support in finding internships and career opportunities in game development for students and graduates of KBTU
- Organization of open educational events for the GameDev community
- Research activities and scientific publications in the field of development and research of video games
GameDev education
The first university in Kazakhstan offering a specialization (minor) in game development and computer graphics.
Undergraduate
As part of the bachelor's degree, students can choose one or more elective disciplines:
development and research video games
- Introduction to game design and development
- Introduction to narrative design
- unity engine basics
- Advanced Unity
- Unreal Engine basics
- Advanced Unreal Engine
- Gamification and Game studies
computer graphics and modeling
- Filmmaking and motion design
- VFX & 3D physics
- object modeling
- 3D character design
- AR/VR
Master's degree
As part of the master's program, students can choose one or more elective disciplines to specialize in game development:
- Accelerated Unity Game Engine
- Accelerated Unreal Engine
- Game engines architecture
- Game programming patterns
- Game graphics programming
- Game systems programming
- XR (AR/VR)
Mentoring gaming projects
Mentoring program for beginners and researchers videogames in GameLab KBTU aims to support both team and individual projects. As part of mentoring, competent support is provided in project management, assistance with production, promotion, and the search for investments and development opportunities. A roadmap is worked out with each resident of the mentoring program, expected results are discussed and regular meetings are held throughout 1 academic year.
Conditions participation:
- Selection produced annually in September
- To participate in the competition, you must submit a concept document of the game or research abstract
- Teams are allowed to participate in the competition, which include at least 1 student / master student / employee of KBTU
Public events
- Summer school on game development for students and graduates
- LEVEL program UP in cooperation with the United Nations Children's Fund (UNICEF) in Kazakhstan
GameLab team KBTU
Alexandra Knysheva - Head of GameLab KBTU. Lecturer in the discipline "Gamification and Video Game Research". MA in Media studies.
Email:
Almaskhan Baimyshev - senior researcher at GameLab KBTU, teacher of master's and undergraduate disciplines in Unreal Engine and game programming architecture. PhD in Robotics, Vanderbilt University. Hyper mobile game developer Pixel Beast and STYX Vertical platformer.
E-mail:
Dmitry Tuchashvili - senior researcher at GameLab KBTU, teacher of master's and undergraduate disciplines in Unity and CG (computer graphics and modeling). MA in Data science. Practicing developer of VR solutions on Unity.
E-mail:
Alexander Mezin - GameLab business consultant KBTU, head of the mentoring program GameLab KBTU. One of the creators of the Edvice knowledge sharing platform in game development .
E-mail:
Vyacheslav Bairamov - a practicing game designer and specialist in narrative design, co-owner of the game studio "TOPchan Games"
E-mail:
Yulia Sutchenko is a teacher of CG -track disciplines (computer graphics and modeling). Practicing Expert AR / VR , XR UX designer at Program Ace is working on the Mapstar project .
E-mail:
Nazim Zhumabayeva - junior researcher at GameLab KBTU. Game developer Iz, resident of the first stream of the mentoring program GameLab KBTU.
E-mail:
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{spoiler=Data Science Laboratory}
LABORATORY OBJECTIVES
To conduct scientific research and/or experimental design and development activities, as well as other scientific, technical, and educational activities.
OBJECTIVES
• To implement national scientific and technological programmes (fundamental, applied, and priority research areas), as well as research tasks carried out within international scientific grants and collaborative projects.
• To support the preparation of Master's and Doctoral dissertations in relevant research fields.
• To conduct a broad range of research in Natural Language Processing (NLP) and contribute to the development of the Natural Language Processing Research School at the
Kazakh-British Technical University.
• To develop innovative AI-based IT solutions for various sectors of the economy targeting both local and global markets.
• To develop novel mathematical models, methodologies, and analytical approaches for data analysis across business and technology domains.
• To conduct joint research with national and international technical, scientific, and socio-economic organizations.
• To create conditions for the commercialization of scientific and technological activities in order to attract industrial partners and investment.
• To participate in international scientific and technological activities.
FUNCTIONS
• Collection and processing of multimodal data and conducting preliminary research.
• Development of intelligent solution prototypes.
• Design and implementation of computational experiments.
• Development, design, and validation of numerical methods and algorithms, including but not limited to Deep Learning, Natural Language Processing, and related artificial intelligence technologies.
• Identification and engagement of potential industrial partners and customers.
• Organization of internships and professional development courses aligned with the laboratory's research areas.
• Participation in national and international research grants and programmes.
Data Science Laboratory Team
Alexander Pak — Laboratory Coordinator, Lead Researcher, Candidate of Technical Sciences. Email:
Shakarim Aubakirov — Senior Researcher
Timur Maratovich Saparov — Senior Researcher
Amirkhan Meirambekuly Serikbay — Junior Researcher
PUBLICATIONS
1. Aubakirov, S., Akhmetov, I., Gelbukh, A. et al. Dynamic optimization of min-df in the GreedSum algorithm for enhanced extractive summarization. Artificial Intelligence Review, vol. 58, art. 270, 2025. https://doi.org/10.1007/s10462-025-11276-w
2. Aubakirov, S., Pak, A., Akhmetov, I., Tleuken, A., Varol, H. A., Akzhalova, A., Karaca, F. Beyond buzzwords: NLP reveals common threads in sustainable and circular construction discourse. PeerJ Computer Science, vol. 11, e3085, 2025. https://doi.org/10.7717/peerj-cs.3085
3. Aubakirov, S., Akhmetov, I. ENTROPY: A New Measure to Gauge Search Engine Optimisation. In: Proceedings of the 21st International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS), Novosibirsk, Russian Federation, 2025, pp. 1--6. https://doi.org/10.1109/OPCS67346.2025.11219388
4. Aubakirov, S., Akhmetov, I., Krassovitsky, A., Gelbukh, A. Entropy--Distance Approach to Evaluating Diversity and Robustness in Organizational Information Retrieval. Computación y Sistemas, vol. 29, no. 4, 2025. https://doi.org/10.13053/cys-29-4-5824
5. Akhmetov, I., Aubakirov, S., Saparov, T., Mussabayev, R., Krassovitsky, A., Gelbukh, A. Generating Ontology from a Set of Texts Belonging to a Certain Field of Knowledge. Computación y Sistemas, vol. 29, no. 4, 2025. https://doi.org/10.13053/cys-29-4-5815
6. Аубакиров Ш., Сейтказиева А. Критический литературный обзор подходов к моделированию кредитного риска в банковском секторе // Банки Казахстана. 2026. № 1–2. С. 6–13.
7. Mukhsimbayev B., Pak A., Kuralbayev A. A Computational Pipeline for Lexical and Thematic Analysis of the Code of Administrative Offenses of the Republic of Kazakhstan // Herald of the Kazakh-British Technical University. - 2025. - Vol. 22, No. 4. - P. 227-243. - DOI: 10.55452/1998-6688-2025-22-4-227-243
8. Toleu A. et al. Noise-Aware Direct Preference Optimization for RLAIF //Applied Sciences. – 2025. – Т. 15. – №. 19. – С. 10328.
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{spoiler=Fuzzy Logic and Intelligent Systems Laboratory (FLIS)}
About the Laboratory
The Fuzzy Logic and Intelligent Systems Laboratory (FLIS) was established in April 2025 at the School of Information Technology and Engineering, KBTU. The laboratory was founded in response to the rapid advancement of artificial intelligence and fuzzy systems, as well as growing interest in both theoretical and applied aspects of intelligent technologies. Prior to its official opening, active research was already underway within the scientific group of Prof. Shamoi P., involving doctoral students, master's students, and undergraduates.
Goals
- Conducting fundamental and applied research in fuzzy logic, artificial intelligence, and intelligent systems;
- Training highly qualified researchers — master's and doctoral students;
- Strengthening KBTU's position as a centre of excellence in AI research on the international stage;
Objectives
- Developing models and algorithms based on fuzzy logic and intelligent systems;
- Publishing research results in high-ranking international journals (Scopus, Web of Science);
- Participating in national and international grant programmes;
- Organising scientific seminars, workshops, and conferences;
- Expanding international research collaboration.
Research Areas
- Fuzzy systems and logic — theory of fuzzy sets, linguistic variables, and fuzzy inference rules; applications in decision-making;
- Color Science — color naming, color perception, color difference, color aesthetics and harmony;
- Color and emotion modelling — study of human emotional responses to visual stimuli and color imagery;
- Intelligent systems — machine learning, neural networks, and hybrid intelligence methods;
- Computer vision and image processing — automatic perception and interpretation of visual information;
- Computational aesthetics — modelling aesthetic preferences and automated evaluation of visual harmony;
- Group fuzzy decision-making — models and algorithms accounting for emotions and preferences in collective decision-making.
Team
• Shamoi Pakizar — Laboratory Head, Professor, PhD;
• Muratbekova Murагуль — Senior Researcher, MSc;
• Yerkin Adilet — Senior Researcher, doctoral student;
• Kadyrgali Elnara — Senior Researcher, doctoral student;
• Ziyada Malika — Researcher, MSc.
Master's and undergraduate students of SITiE KBTU also actively participate in the laboratory's research activities. To date, more than 60 researchers are involved in the work of the laboratory, including current students and alumni of the university.
Projects
• IRN AP22786412
Publications
2026:
• Muratbekova M., Toganas N., Igali A., Shagyrov M., Kadyrgali E., Yerkin A. et al. Color models in image processing: A review and experimental comparison. Discover Applied Sciences, 2026.
• Maratuly T., Shamoi P., Samigulin T. Fuzzy expert system for the process of collecting and purifying acidic water: a digital twin approach. International Journal of System Assurance Engineering and Management, 2026.
2025:
• Shamoi P., Toganas N., Muratbekova M., Kadyrgali E., Yerkin A., Igali A. et al. Colibri fuzzy model: Color linguistic-based representation and interpretation. IEEE Access, 13, 205932–205956, 2025.
• Akram A., Kozhamuratova A., Shamoi P. Integrating Kansei engineering in web design for enhanced logistics services. Service Oriented Computing and Applications, 2025.
• Ogorodova A., Shamoi P., Karatayev A. Fuzzy Intelligent System for Student Software Project Evaluation. International Journal of Modern Education and Computer Science, 2025.
• Konyspay A., Shamoi P., Ziyada M., Smambayev Z. Meme Similarity and Emotion Detection using Multimodal Analysis. Activity and Behaviour Computing, 2025.
• Em I., Toganas N., Shamoi P. Emotion Classification in Digital Art using Color Features and Machine Learning. IEEE SIST, 2025.
• Burambekova A., Shamoi P. Comparative Analysis of Color Models for Human Perception and Visual Color Difference. IEEE SIST, 2025.
• Sagatbek A., Seidakhmetova A., Shamoi P. Comparative Analysis of Clustering Algorithms for Human-Consistent Dominant Color Extraction. IEEE SIST, 2025.
• Aliyev I., Muradova G., Aliyeva S., Mustafazada S., Smambayev Z., Shamoi P. Public Perception of Feminism using Sentiment and Emotion Analysis. IEEE SIST, 2025.
• Torekhan Y., Altynbekov N., Shamoi P. Aesthetic Index for Art Paintings Using Visual Features. IEEE SIST, 2025.
International Collaboration
• Atsushi Inoue — Kyushu Institute of Technology, Japan. Area: fuzzy theory. Collaboration since 2011.
• Kaori Yoshida — Kyushu Institute of Technology, Japan. Area: applied perception. Collaboration since 2024.
• Jamalladin Hasanov — ADA University, Azerbaijan. Area: computer vision, color. Collaboration since 2022.
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{spoiler=Cybersecurity Laboratory}
About the Laboratory
The Cybersecurity Laboratory is a research unit of the School of Information Technology and Engineering at KBTU, established in response to contemporary challenges in information and national security. The Laboratory conducts both fundamental and applied research, develops innovative information security technologies, and provides an advanced research environment for training highly qualified specialists in cybersecurity and digital technologies.
Research Areas
The Laboratory conducts research in the following areas:
• Information security and data protection;
• National security and cyber defence;
• Intelligent threat monitoring and analysis systems;
• Artificial intelligence and machine learning for cybersecurity applications;
• Development of digital platforms and information systems;
• Medical information technologies and healthcare data protection;
• Computer vision, image processing, and biometric data analysis;
• Sustainable agricultural technologies and the digital transformation of livestock farming.
Research Activities
The Laboratory carries out interdisciplinary research at the intersection of cybersecurity, artificial intelligence, and advanced computing technologies. Emphasis is placed on developing digital platforms for production lifecycle management, ensuring data integrity, applying machine learning techniques for image analysis, biometric authentication, medical diagnostics, and intelligent information processing.
Research outcomes are regularly presented at leading international conferences, including IEEE, ICCE Asia, SIST, CIEES, EEPES, and other internationally recognised scientific forums.
Research Projects
The Laboratory is currently implementing the following major government programme-targeted research project:
BR28712579 — "Enhancing the Sustainability of Livestock Development through the Development of an Integrated Digital Information Platform Supporting the Entire Production Lifecycle" (2025–2027).
The project aims to establish a modern digital ecosystem for the agricultural sector by integrating artificial intelligence, cybersecurity, and data management technologies to improve the efficiency, sustainability, and resilience of livestock production.
Laboratory Team
Mukasheva Assel Koptleuvna serves as the Project Leader and Laboratory Coordinator. Email:
The Laboratory team comprises experienced scientists, senior and leading researchers, engineers, and early-career researchers engaged in research projects in cybersecurity, artificial intelligence, digital technologies, and engineering solutions. The multidisciplinary team includes more than 30 specialists from various scientific and engineering disciplines.
International Collaboration
The Laboratory actively collaborates with international universities and research organisations. Key partners include:
• University of Cincinnati (USA) – scientific collaboration and doctoral research supervision;
• The Korean Institute of Electrical Engineers – Electric Facility Society (Republic of Korea) – collaborative research on adaptive traffic management algorithms using artificial intelligence and computer vision technologies.
Research Achievements
Laboratory researchers regularly publish their findings in leading international journals and conferences indexed in Scopus and Web of Science. During the reporting period, publications appeared in Q1 and Q2 journals covering topics such as:
• Medical data security;
• Intelligent biometric authentication systems;
• Computer vision and medical image segmentation;
• Artificial intelligence-based forecasting;
• Information system vulnerability assessment;
• Deep learning and signal processing;
• Digital transformation of transportation and industrial systems.
The Laboratory also actively participates in international scientific conferences and annually presents dozens of research papers addressing emerging challenges and advances in information technology.
Training Young Researchers
One of the Laboratory's strategic priorities is the active involvement of undergraduate, master's, and doctoral students in research activities. Young researchers participate in government-funded projects, contribute to scientific publications, develop software and intelligent systems, and present their research findings at international scientific conferences.
Mission
The mission of the Cybersecurity Laboratory is to advance cutting-edge research in cybersecurity, artificial intelligence, and digital technologies; develop innovative solutions for information protection and the sustainable development of the digital economy; and educate highly qualified specialists capable of addressing the complex cybersecurity challenges of the modern digital world.
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{spoiler=Computational Neuroscience Laboratory}
About the Laboratory
The Computational Neuroscience Laboratory was established in 2023 within the School of Information Technology and Engineering at KBTU to promote interdisciplinary research integrating modern machine learning, artificial intelligence, and data analytics with advanced neuroimaging and neurophysiological technologies. The laboratory investigates the complex mechanisms of brain function, cognitive processes, and develops intelligent methods for diagnosing and analysing neural activity.
Main Research Areas
The laboratory conducts research in the following areas:
• Analysis and interpretation of neuroimaging and neurophysiological data using advanced machine learning algorithms.
• Investigation of the mechanisms underlying information encoding and transmission in the brain.
• Research on cognitive processes, emotional perception, and decision-making.
• Analysis of electroencephalography (EEG), functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and functional near-infrared spectroscopy (fNIRS).
• Development of intelligent diagnostic methods for neurodegenerative diseases and neurocognitive disorders.
• Application of nonlinear brain signal analysis and artificial intelligence technologies to investigate brain development and ageing.
Research Activities
The laboratory conducts research on brain activity during resting-state and cognitive task performance, investigates emotional regulation and attention processes, and develops machine learning methods for identifying biomarkers of brain disorders.
Attention is devoted to multimodal studies involving patients with brain tumours and multiple sclerosis by integrating structural MRI, functional MRI, EEG, and other neuroimaging modalities for preoperative cognitive function mapping and improved diagnostic accuracy.
Research Projects
The laboratory participates in the following national research projects:
• AP23486255 "Investigation of Brain Network Functioning and Cognitive Function Mapping in Patients with Brain Tumours" (2024–2026) – aimed at developing non-invasive diagnostic methods and preoperative cognitive function mapping.
• BR27198099 "Development of Integrative Research in Neuroscience" (2024–2026) – a targeted research programme focused on applying artificial intelligence and multimodal brain signal analysis to investigate brain development, ageing, and neurodegenerative diseases.
Laboratory Team
The laboratory is coordinated by:
• Diana Arman, PhD — Coordinator of the Computational Neuroscience Laboratory and Head of the research subgroup on brain activity analysis and artificial intelligence applications in neuroscience.
International Collaboration
The laboratory actively collaborates with leading international research centers and universities, including:
• MRC Cognition and Brain Sciences Unit, University of Cambridge (United Kingdom);
• George Mason University (USA).
International projects involve researchers from the United States and the United Kingdom. Laboratory members regularly present their research findings at leading international conferences, including OHBM, FENS, and ICON.
Research Achievements
Research findings are published in leading international journals indexed in Scopus and Web of Science. During the reporting period, laboratory researchers published Q1 journal articles covering EEG and fMRI analysis, depression, age-related brain changes, cognitive functions, and the application of machine learning methods in neuroscience.
In addition, the laboratory has obtained copyright certificates for developments in neural data analysis and intelligent cognitive diagnostic systems.
Training in Young Researchers
The laboratory actively engages master’s and PhD students in research activities. Current projects include EEG analysis during emotional regulation, investigation of brain activity during decision-making, and functional MRI studies of patients with brain tumours.
The laboratory regularly organises scientific seminars, educational events, and training programmes for early-career researchers in computational neuroscience.
Mission
The mission of the Computational Neuroscience Laboratory is to advance interdisciplinary research at the intersection of artificial intelligence, neuroscience, and data science by developing intelligent methods for brain diagnostics and computational modelling, while educating highly qualified specialists capable of addressing contemporary challenges in medicine and digital technologies.
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